Naturalistic Yielding Behavior of Drivers at an Unsignalized Intersection based on Survival Analysis

Delgermaa Gankhuyag; Cristina Olaverri-Monreal

doi:10.48550/arXiv.2307.10879

Naturalistic Yielding Behavior of Drivers at an Unsignalized Intersection based on Survival Analysis

Delgermaa Gankhuyag, Cristina Olaverri-Monreal

Department Intelligent Transport Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

In recent years, autonomous vehicles have become increasingly popular, leading to extensive research on their safe and efficient operation. Understanding road yielding behavior is crucial for incorporating the appropriate driving behavior into algorithms. This paper focuses on investigating drivers' yielding behavior at unsignalized intersections. We quantified and modelled the speed reduction time for vulnerable road users at a zebra crossing using parametric survival analysis. We then evaluated the impact of speed reduction time in two different interaction scenarios, compared to the baseline condition of no interaction through an accelerated failure time regression model with the log-logistic distribution. The results demonstrate the unique characteristics of each yielding behavior scenario, emphasizing the need to account for these variations in the modelling process of autonomous vehicles.

Original language	English
Title of host publication	2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)
Number of pages	7
DOIs	https://doi.org/10.48550/arXiv.2307.10879
Publication status	Published - Sept 2023

Publication series

Name	IEEE

Fields of science

303 Health Sciences
303008 Ergonomics
201306 Traffic telematics
202031 Network engineering
202036 Sensor systems
202038 Telecommunications
202040 Transmission technology
203 Mechanical Engineering
211908 Energy research
211911 Sustainable technologies
102 Computer Sciences
102001 Artificial intelligence
102002 Augmented reality
102003 Image processing
102013 Human-computer interaction
102015 Information systems
102019 Machine learning
102021 Pervasive computing
102024 Usability research
102026 Virtual reality
102029 Practical computer science
102034 Cyber-physical systems
501026 Psychology of perception
501 Psychology
501025 Traffic psychology
201305 Traffic engineering
202 Electrical Engineering, Electronics, Information Engineering
202003 Automation
202030 Communication engineering
202034 Control engineering
202035 Robotics
202037 Signal processing
202041 Computer engineering
203004 Automotive technology
211902 Assistive technologies
211909 Energy technology
211917 Technology assessment
501030 Cognitive science

JKU Focus areas

Digital Transformation
Sustainable Development: Responsible Technologies and Management

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.48550/arXiv.2307.10879

Cite this

@inproceedings{25be960e0ff448b1afde1a46e1ffefcd,

title = "Naturalistic Yielding Behavior of Drivers at an Unsignalized Intersection based on Survival Analysis",

abstract = "In recent years, autonomous vehicles have become increasingly popular, leading to extensive research on their safe and efficient operation. Understanding road yielding behavior is crucial for incorporating the appropriate driving behavior into algorithms. This paper focuses on investigating drivers' yielding behavior at unsignalized intersections. We quantified and modelled the speed reduction time for vulnerable road users at a zebra crossing using parametric survival analysis. We then evaluated the impact of speed reduction time in two different interaction scenarios, compared to the baseline condition of no interaction through an accelerated failure time regression model with the log-logistic distribution. The results demonstrate the unique characteristics of each yielding behavior scenario, emphasizing the need to account for these variations in the modelling process of autonomous vehicles.",

author = "Delgermaa Gankhuyag and Cristina Olaverri-Monreal",

year = "2023",

month = sep,

doi = "10.48550/arXiv.2307.10879",

language = "English",

series = "IEEE",

booktitle = "2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)",

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T1 - Naturalistic Yielding Behavior of Drivers at an Unsignalized Intersection based on Survival Analysis

AU - Gankhuyag, Delgermaa

AU - Olaverri-Monreal, Cristina

PY - 2023/9

Y1 - 2023/9

N2 - In recent years, autonomous vehicles have become increasingly popular, leading to extensive research on their safe and efficient operation. Understanding road yielding behavior is crucial for incorporating the appropriate driving behavior into algorithms. This paper focuses on investigating drivers' yielding behavior at unsignalized intersections. We quantified and modelled the speed reduction time for vulnerable road users at a zebra crossing using parametric survival analysis. We then evaluated the impact of speed reduction time in two different interaction scenarios, compared to the baseline condition of no interaction through an accelerated failure time regression model with the log-logistic distribution. The results demonstrate the unique characteristics of each yielding behavior scenario, emphasizing the need to account for these variations in the modelling process of autonomous vehicles.

AB - In recent years, autonomous vehicles have become increasingly popular, leading to extensive research on their safe and efficient operation. Understanding road yielding behavior is crucial for incorporating the appropriate driving behavior into algorithms. This paper focuses on investigating drivers' yielding behavior at unsignalized intersections. We quantified and modelled the speed reduction time for vulnerable road users at a zebra crossing using parametric survival analysis. We then evaluated the impact of speed reduction time in two different interaction scenarios, compared to the baseline condition of no interaction through an accelerated failure time regression model with the log-logistic distribution. The results demonstrate the unique characteristics of each yielding behavior scenario, emphasizing the need to account for these variations in the modelling process of autonomous vehicles.

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DO - 10.48550/arXiv.2307.10879

M3 - Conference proceedings

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BT - 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)

ER -